Define demographic
variables
Need to check
datasets with Rcihi (why do we have QC in L1? Am I using not the latest
dataset?)
all <- filtered
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]
ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="") + theme_bw()

table(all$L1,all$Context)
English in Germany English in Italy German in Australia Italian in Australia
Afrikaans 0 0 1 0
Albanian 0 1 0 0
Cantonese 0 0 2 0
Chinese 0 2 2 0
Dutch 1 0 0 0
English 1 0 75 73
English and Dutch 0 0 2 0
German 64 0 0 0
German and English 1 0 1 0
I 0 0 0 1
Indonesian 0 0 1 0
Italian 0 87 0 0
Japanese 0 0 1 0
Mandarin 0 0 1 1
Persian (Farsi) 0 0 1 0
Romanian 0 0 1 0
Russian 2 0 0 0
Sindhi 0 0 1 0
Spanish 1 0 0 0
Turkish 1 0 0 0
Ukrainian 0 1 0 0
table(all$degree,all$L1)
Afrikaans Albanian Cantonese Chinese Dutch English English and Dutch German German and English I
BA in Anglistik 0 0 0 0 0 1 0 34 1 0
BA in Nordamerikastudien 0 0 0 0 0 0 0 4 0 0
HUM 1 0 2 0 0 93 1 0 0 1
HUM.SCI 0 0 0 0 0 6 0 0 0 0
LA 0 0 0 0 1 0 0 25 0 0
Lingue e letterature straniere 0 1 0 1 0 0 0 0 0 0
Lingue, mercati e culture dell'Asia 0 0 0 1 0 0 0 0 0 0
SCI 0 0 0 2 0 47 1 0 1 0
Indonesian Italian Japanese Mandarin Persian (Farsi) Romanian Russian Sindhi Spanish Turkish
BA in Anglistik 0 0 0 0 0 0 2 0 1 0
BA in Nordamerikastudien 0 0 0 0 0 0 0 0 0 0
HUM 0 0 0 0 0 0 0 0 0 0
HUM.SCI 0 0 0 0 0 0 0 0 0 0
LA 0 0 0 0 0 0 0 0 0 1
Lingue e letterature straniere 0 75 0 0 0 0 0 0 0 0
Lingue, mercati e culture dell'Asia 0 12 0 0 0 0 0 0 0 0
SCI 1 0 1 2 1 1 0 1 0 0
Ukrainian
BA in Anglistik 0
BA in Nordamerikastudien 0
HUM 0
HUM.SCI 0
LA 0
Lingue e letterature straniere 1
Lingue, mercati e culture dell'Asia 0
SCI 0
- Check for L1 but we decided not to filter for it
#Filter by L1
nc <- names(table(all$Context))
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
72 91 89 75
l1_filter <- all[(all$Context == nc[1] & (all$L1 == "German" | all$L1 == "German and English")) |
(all$Context == nc[2] & (all$L1 == "Italian")) |
(all$Context == nc[3] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")) |
(all$Context == nc[4] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")),]
#all <- l1_filter
# do not filter for L1
all <- all
# subset demographics
demo <- subset(all,select=c("Resp.ID",demographics_var,l2School_variables))
# Numeri finali
table(l1_filter$Context)
English in Germany English in Italy German in Australia Italian in Australia
65 87 78 73
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
72 91 89 75
- Filter missing value:
- Filter participants who didn’t put the degree
- we don’t care about speak.other.L2 and study.other.L2
missing_bySample <- rowSums(is.na(demo))
names(missing_bySample) <- demo$Resp.ID
missing_byVar <- colSums(is.na(demo))
names(missing_byVar) <- colnames(demo)
barplot(missing_bySample)

d <- data.frame(miss=missing_byVar)
d$varID <- rownames(d)
ggplot(data=d,aes(x=varID,y=miss)) + geom_bar(stat="identity") + theme_bw() +theme(axis.text.x = element_text(angle = 45, hjust = 1))

demo_missing <- demo %>% group_by(Context) %>% summarise(roleL2.degree_na = sum(is.na(roleL2.degree)),
L2.VCE_na = sum(is.na(L2.VCE)),
other5.other.ways_na=sum(is.na(other5.other.ways )),
uni1.year_na = sum(is.na(uni1.year)),
primary1.L2school_na=sum(is.na(primary1.L2school)),
CLS3.L2school_na = sum(is.na(CLS3.L2school)),
VSL4.L2school_na=sum(is.na(VSL4.L2school)),
degree = sum(is.na(degree)),
schooL2country5.L2school_na=sum(is.na(schooL2country5.L2school)))
# We do not filter for speak.other.L2 or study.other.L2
#demo[is.na(demo$speak.other.L2),]
# teniamo
#demo[is.na(demo$study.other.L2),]
missing_bySample[names(missing_bySample) == "5166861581"]
5166861581
10
#demo[is.na(demo$year.studyL2),]
missing_bySample[names(missing_bySample) == "5378798787"]
5378798787
3
# remove NA from degree
#table(demo$degree,useNA = "always")
# Remove people
all <- all[!is.na(all$degree),]
Stats about filtered
dataset
kable(table(all$Context))
| English in Germany |
70 |
| English in Italy |
91 |
| German in Australia |
88 |
| Italian in Australia |
74 |
kable(table(all$study.year))
| 1st semester |
70 |
| 1st year |
253 |
kable(table(all$year.studyL2))
| 0 years |
33 |
| 1- 3 years |
9 |
| 1-3 years |
7 |
| 4-6 years |
53 |
| First year of primary school |
73 |
| Kindergarten |
29 |
| Less than a year |
18 |
| more than 6 years |
41 |
| Other |
59 |
Recoded demographic
variables
recoded_dem_richi <- read_excel("02-descriptive_data/21 03 merged_filtered_imputedMedian_likertNumber.xlsx")
Write filtered and
merged dataset
write.csv(all,file.path("02-descriptive_data/context-merged_filtered.csv"))
Descriptive plots and
tables
all$speak.other.L2_binary <- ifelse(!is.na(all$speak.other.L2) &
!(all$speak.other.L2 %in% c("Yes","No")),"Yes",as.character(all$speak.other.L2))
kable(table(all$speak.other.L2_binary,all$Context,useNA = "always"))
| No |
12 |
24 |
52 |
53 |
0 |
| Yes |
57 |
67 |
36 |
20 |
0 |
| NA |
1 |
0 |
0 |
1 |
0 |
NA
tabAge <- t(table(all$Age,all$Context))
ggdf <- data.frame(Age = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])
ggplot(ggdf,aes(x=Age,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by age")+
geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))

# add numbers on the bar
tabAge <- t(table(all$Gender,all$Context))
ggdf <- data.frame(Gender = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])
ggplot(ggdf,aes(x=Gender,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by gender")+ geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))

# add numbers on the bar
tabAge <- t(table(all$origins,all$Context))
ggplot(all,aes(x=origins,fill=Context)) + geom_bar(position="dodge",colour="white") + ggtitle("Origins by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=60, width=0.3, height=0.4) + ggtitle("Participants by origins")

tabAge
No Yes
English in Germany 65 5
English in Italy 90 1
German in Australia 63 25
Italian in Australia 36 38
tabAge <- t(table(all$prof,all$Context))
ggplot(all,aes(x=Context,fill=prof)) + geom_bar(position="dodge",colour="white") + ggtitle("Proficiency by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)

tabAge
Advanced Elementary Intermediate Upper-intermediate
English in Germany 38 0 5 27
English in Italy 23 2 9 57
German in Australia 4 32 25 27
Italian in Australia 0 29 29 16
tabAge <- t(table(all[all$Context != "English in Germany" & all$Context != "English in Italy","L2.VCE"],all[all$Context != "English in Germany" & all$Context != "English in Italy",'Context'],useNA = "always"))
tabAge <- tabAge[-3,]
ggplot(all[all$Context != "English in Germany" & all$Context != "English in Italy",],aes(x=Context,fill=L2.VCE)) + geom_bar(position="dodge",colour="white") + ggtitle("L2.VCE by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)

- da mettere a posto con Richi
# year study L2
table(all$year.studyL2,all$other.year.studyL2.richi)
BILINGUAL FIRST.YEAR.SECONDARY FOURTH.YEAR.PRIMARY LOWER.SECONDARY PERSONAL SECOND.YEAR.PRIMARY
0 years 0 0 0 0 0 0
1- 3 years 0 0 0 0 0 0
1-3 years 0 0 0 0 0 0
4-6 years 0 0 0 0 0 0
First year of primary school 0 0 0 0 0 0
Kindergarten 0 0 0 0 0 0
Less than a year 0 0 0 0 0 0
more than 6 years 0 0 0 0 0 0
Other 4 10 5 4 2 2
SECOND.YEAR.SECONDARY THIRD.YEAR.PRIMARY
0 years 0 0
1- 3 years 0 0
1-3 years 0 0
4-6 years 0 0
First year of primary school 0 0
Kindergarten 0 0
Less than a year 0 0
more than 6 years 0 0
Other 2 28
all$year.studyL2 <- ifelse(all$year.studyL2 == "Other",all$other.year.studyL2.richi,all$year.studyL2 )
# European context
ggplot(all[all$Context == "English in Germany" | all$Context == "English in Italy",],aes(x=degree,fill=year.studyL2)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree by study year L2, by Context") + facet_grid(~Context,scales="free") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(y = "N participants", x = "degree")

# Australian context
tabAge <- t(table(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'degree'],all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'Context']))
ggplot(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in Australian Contexts") + draw_grob(tableGrob(tabAge), x=1., y=40, width=0.3, height=0.4)

tabAge
HUM HUM.SCI SCI
German in Australia 48 4 36
Italian in Australia 50 2 22
# Australian context
tabAge <- t(table(all[all$Context == "English in Italy" | all$Context == "English in Germany",'degree'],all[all$Context == "English in Italy" | all$Context == "English in Germany",'Context']))
ggplot(all[all$Context == "English in Italy" | all$Context == "English in Germany",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in European Contexts")

tabAge
BA in Anglistik BA in Nordamerikastudien LA Lingue e letterature straniere Lingue, mercati e culture dell'Asia
English in Germany 39 4 27 0 0
English in Italy 0 0 0 78 13
Likert scales
- Convert Likert scales to numbers
convertToNumber <- function(column){
column <- factor(column,levels = c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
column_number <- as.numeric(column)
return(column_number)
}
table(all$Context)
English in Germany English in Italy German in Australia Italian in Australia
70 91 88 74
table(all$study.year)
1st semester 1st year
70 253
convert_likert <- data.frame(apply(subset(all,select=likert_variables_all),2,convertToNumber))
colnames(convert_likert) <- paste0(colnames(convert_likert),"1")
likert_variables1 <- paste0(likert_variables_all,"1")
# join the converted variables to the filtered dataset
filtered_conv <- cbind(all,convert_likert)
table(filtered_conv[,likert_variables_all[4]],filtered_conv[,likert_variables1[4]],useNA = "always")
1 2 3 4 5 <NA>
Agree 0 0 0 121 0 0
Disagree 0 10 0 0 0 0
Not sure 0 0 39 0 0 0
Strongly agree 0 0 0 0 152 0
Strongly disagree 1 0 0 0 0 0
<NA> 0 0 0 0 0 0
write.csv(filtered_conv,"02-descriptive_data/merged_filtered_likertNumber.csv",row.names = FALSE)
Impute missing values
- Using median values
The missing values appears to be at random and there are max two
missing values in one variable (see plots below). In order not to loose
12 participants while doing the factor analysis across contexts it is
preferable to impute the 12 missing values.
all <- filtered_conv
# Items to use for factor analysis : items shared between contexts
# items to be used for the FA
usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
rownames(all) <- all$Resp.ID
usable_data_context <- all[,c(usable_items,"Context")]
dat_noNA <- usable_data_context[rowSums(is.na(usable_data_context)) == 0,]
all_noNA <- all[rowSums(is.na(usable_data_context)) == 0,]
table(rowSums(is.na(usable_data_context)))
0 1
311 12
# Participants with NA to remove
table(rowSums(is.na(usable_data_context)),usable_data_context$Context,useNA = "always")
English in Germany English in Italy German in Australia Italian in Australia <NA>
0 70 85 87 69 0
1 0 6 1 5 0
<NA> 0 0 0 0 0
# Variable missing values
table(colSums(is.na(usable_data_context)))
0 1 2
19 10 1
table(rowSums(is.na(usable_data_context)),usable_data_context$Context,useNA = "always")
English in Germany English in Italy German in Australia Italian in Australia <NA>
0 70 85 87 69 0
1 0 6 1 5 0
<NA> 0 0 0 0 0
# check what to use to impute
# have a look at the distribution of missing values
library(mice)
library(VIM)
mice_plot <- aggr(usable_data_context[,usable_items], col=c('navyblue','yellow'),
numbers=TRUE, sortVars=TRUE,
labels=names(usable_data_context[,usable_items]), cex.axis=.4,
gap=1, ylab=c("Missing data","Pattern"),cex.numbers=0.5)
Variables sorted by number of missings:

# Imputing using median
library(Hmisc)
imputedMedian <- usable_data_context
imputedMedian$globalaccess.post1 <- with(imputedMedian[,usable_items], impute(globalaccess.post1, median))
imputedMedian$citizen.post1 <- with(imputedMedian[,usable_items], impute(citizen.post1, median))
imputedMedian$money.instru1 <- with(imputedMedian[,usable_items], impute(money.instru1, median))
imputedMedian$knowledge.instru1 <- with(imputedMedian[,usable_items], impute(knowledge.instru1, median))
imputedMedian$life.intr1 <- with(imputedMedian[,usable_items], impute(life.intr1, median))
imputedMedian$time.integr1 <- with(imputedMedian[,usable_items], impute(time.integr1, median))
imputedMedian$expect.ought1 <- with(imputedMedian[,usable_items], impute(expect.ought1, median))
imputedMedian$job.instru1 <- with(imputedMedian[,usable_items], impute(job.instru1, median))
imputedMedian$career.instru1 <- with(imputedMedian[,usable_items], impute(career.instru1, median))
imputedMedian$meeting.integr1 <- with(imputedMedian[,usable_items], impute(meeting.integr1, median))
imputedMedian$interact.post1 <- with(imputedMedian[,usable_items], impute(interact.post1, median))
# check before after
table(imputedMedian$time.integr1)
2 3 4 5
3 21 95 204
table(usable_data_context$time.integr1)
2 3 4 5
3 21 95 202
table(imputedMedian$life.intr1)
1 2 3 4 5
8 79 81 117 38
table(usable_data_context$life.intr1)
1 2 3 4 5
8 79 80 117 38
table(imputedMedian$knowledge.instru1)
1 2 3 4 5
1 2 29 189 102
table(usable_data_context$knowledge.instru1)
1 2 3 4 5
1 2 29 188 102
table(imputedMedian$money.instru1)
1 2 3 4 5
3 38 179 84 19
table(usable_data_context$money.instru1)
1 2 3 4 5
3 38 178 84 19
table(imputedMedian$citizen.post1)
1 2 3 4 5
3 22 75 148 75
table(usable_data_context$citizen.post1)
1 2 3 4 5
3 22 75 147 75
table(imputedMedian$globalaccess.post1)
1 2 3 4 5
1 3 20 159 140
table(usable_data_context$globalaccess.post1)
1 2 3 4 5
1 3 20 158 140
table(imputedMedian$expect.ought1)
1 2 3 4 5
126 142 30 21 4
table(usable_data_context$expect.ought1)
1 2 3 4 5
126 141 30 21 4
table(imputedMedian$job.instru1)
2 3 4 5
13 103 133 74
table(usable_data_context$job.instru1)
2 3 4 5
13 103 132 74
table(imputedMedian$career.instru1)
1 2 3 4 5
1 1 63 131 127
table(usable_data_context$career.instru1)
1 2 3 4 5
1 1 63 130 127
table(imputedMedian$meeting.integr1)
2 3 4 5
1 10 121 191
table(usable_data_context$meeting.integr1)
2 3 4 5
1 10 121 190
table(imputedMedian$interact.post1)
2 3 4 5
1 19 140 163
table(usable_data_context$interact.post1)
2 3 4 5
1 19 140 162
- Substitute imputed data for the common variables to be used in the
Factor Analysis
all <- all[,!(colnames(all) %in% usable_items)]
imputedMedian$Context <- NULL
sum(!(colnames(imputedMedian) %in% usable_items))
[1] 0
all <- cbind(all,imputedMedian[match(rownames(imputedMedian),all$Resp.ID),])
Add some updates that Richi did in Date 7th June
2018
> Other_ways_and_degree_role_with_respondent_IDs <- read_excel("Other-ways-and-degree-role-with-respondent-IDs.xlsx")
> sum(Other_ways_and_degree_role_with_respondent_IDs$Resp.ID != Other_ways_and_degree_role_with_respondent_IDs$Resp.ID__1)
> # to replace
> # match for the NA degree.role
>
> match_updates <- match(all$Resp.ID,Other_ways_and_degree_role_with_respondent_IDs$Resp.ID)
> all$private.lessons1.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$private.lessons1.other.ways
> all$study.holiday2.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$study.holiday2.other.ways
> all$year.sem.abroad3.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$year.sem.abroad3.other.ways
> all$online.course4.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$online.course4.other.ways
> all$other5.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$other5.other.ways
> all$degree.role[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$degree.role
Save imputed
data
write.csv(all,"02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv",row.names = FALSE)
Barplot of likert
variables
all_melt <- melt(all,id.vars = c("Resp.ID","Gender","Age","prof","Context","study.year"),
measure.vars = likert_variables1)
Warning: attributes are not identical across measure variables; they will be dropped
all_melt$value <- factor(all_melt$value,levels=c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
# dim(all_melt)
# 323*length(likert_variables1)
all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 646 rows [9368, 9369, 9370, 9371, 9372, 9373, 9374, 9375, 9376, 9377, 9378, 9379, 9380, 9381, 9382, 9383, 9384, 9385, 9386, 9387, ...].
ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack",colour="black") +
facet_grid(Context~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + ggtitle("Filtered dataset") + scale_fill_manual(values=c("#ca0020","#f4a582","#ffffbf","#abd9e9","#2c7bb6","grey"))

filt_sum <- all_melt %>% group_by(Context,variable,type,value) %>% dplyr::summarise(Ngroup=length(value))
`summarise()` has grouped output by 'Context', 'variable', 'type'. You can override using the `.groups` argument.
ggplot(filt_sum,aes(x=value,y=Ngroup,colour=Context,group=interaction(variable, Context))) + geom_line() + geom_point() + facet_wrap(~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1))

Barplot of Educated
and Necessity in the Australian and European Contexts
# add numbers on the bar
educated <- all[all$Context %in% c("German in Australia","Italian in Australia"),]
table(educated$educated1,educated$Context,useNA="always")
German in Australia Italian in Australia <NA>
1 11 9 0
2 25 24 0
3 12 13 0
4 29 18 0
5 11 10 0
<NA> 0 0 0
educated$educated1 <- factor(educated$educated1,levels = c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
tabEdu <- t(table(educated$educated1,educated$Context))
ggdf <- data.frame(Educated = rep(colnames(tabEdu),each=2),
N.Participants = as.numeric(tabEdu),
Context = rep(rownames(tabEdu),times=5))
ggplot(ggdf,aes(x=Educated,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Educated by Context")+ geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))

ggplot(ggdf,aes(x=Context,y=N.Participants,fill=Educated)) + geom_bar(position="dodge",colour="white",stat="identity") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Educated by Context")+ geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))

# add numbers on the bar
necessity <- all[all$Context %in% c("English in Germany","English in Italy"),]
table(necessity$necessity1,necessity$Context,useNA="always")
English in Germany English in Italy <NA>
1 1 12 0
2 7 16 0
3 13 6 0
4 32 36 0
5 16 20 0
<NA> 1 1 0
necessity$necessity1 <- factor(necessity$necessity1,levels = c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
tabNec <- t(table(necessity$necessity1,necessity$Context,useNA = "always"))[-3,]
ggdf <- data.frame(Necessity = rep(colnames(tabNec),each=2),
N.Participants = as.numeric(tabNec),
Context = rep(rownames(tabNec),times=6))
ggplot(ggdf,aes(x=Necessity,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,40,10),limits=c(0,40)) + theme_bw() + ggtitle("Necessity by Context")+ geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))

ggplot(ggdf,aes(x=Context,y=N.Participants,fill=Necessity)) + geom_bar(position="dodge",colour="white",stat="identity") + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Necessity by Context")+ geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9))

Correlation plot of
items by context
Italian in
Australia
cov <- cor(filtered_conv[filtered_conv$Context == "Italian in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
data_cor_ita_in_au <- data.frame(cor_ita_in_au=cov[lower.tri(cov, diag = TRUE)],
var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
#pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE], annotation_colors = ann_colors_wide,breaks=seq(-1,1,0.2),col=c("#67001f","#b2182b","#d6604d","#f4a582","#fddbc7","#f7f7f7","#d1e5f0","#92c5de","#4393c3","#2166ac","#053061"),show_colnames = FALSE,width = 7,height = 7)
###################
diag(cov) <- NA
pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

cov_ItaAus <- cov
German in
Australia
cov <- cor(filtered_conv[filtered_conv$Context == "German in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
data_cor_germ_in_au <- data.frame(cor_germ_in_au=cov[lower.tri(cov, diag = TRUE)],
var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "German in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

#
cov_GermAus <- cov
English in
Germany
cov <- cor(filtered_conv[filtered_conv$Context == "English in Germany",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1", "speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
data_cor_eng_in_germ <- data.frame(cor_eng_in_germ=cov[lower.tri(cov, diag = TRUE)],
var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(id1="#f6e8c3",necessity="#b35806",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "English in Germany",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

cov_EngGerm <- cov
English in Italy
cov <- cor(filtered_conv[filtered_conv$Context == "English in Italy",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1","speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
data_cor_eng_in_ita <- data.frame(cor_eng_in_ita=cov[lower.tri(cov, diag = TRUE)],
var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- "necessity"
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",necessity="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "English in Italy",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

#
cov_EngIta <- cov
Correlation between
correlation in the different contexts
We will perform an exploratory FA combining all the contexts
together. This means that we are assuming that the correlation between
items across context has the same direction (does not happen that
cor(item1,item2)_context1 > 0 and cor(item1,item2)_context2 <
0).
common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))
[1] 0
sum(rownames(cov_EngIta) != rownames(cov_GermAus))
Warning: longer object length is not a multiple of shorter object length
[1] 4
sum(rownames(cov_EngIta) != rownames(cov_ItaAus))
Warning: longer object length is not a multiple of shorter object length
[1] 4
sum(rownames(cov_GermAus) != rownames(cov_ItaAus))
[1] 0
common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]
sum(rownames(common_EngIta) != colnames(common_EngIta))
[1] 0
sum(rownames(common_EngGerm) != colnames(common_EngGerm))
[1] 0
sum(rownames(common_GermAus) != colnames(common_GermAus))
[1] 0
sum(rownames(common_ItaAus) != colnames(common_ItaAus))
[1] 0
par(mfrow=c(2,3))
plot(common_EngIta,common_EngGerm)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngGerm,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngGerm,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_GermAus,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

# Largest differences
low_EngGerm <- lower.tri(common_EngGerm)
common_EngGerm[!low_EngGerm] <- NA
common_GermAus[!(lower.tri(common_GermAus))] <- NA
common_ItaAus[!(lower.tri(common_ItaAus))] <- NA
common_EngIta[!(lower.tri(common_EngIta))] <- NA
mat <- data.frame(common_EngGerm=c(common_EngGerm),
common_EngIta=c(common_EngIta),
common_GermAus = c(common_GermAus),
common_ItaAus = c(common_ItaAus),
compare = paste(rownames(common_EngGerm),colnames(common_EngGerm),sep=".")) %>%
filter(!is.na(common_EngGerm)) %>%
separate(compare,into=c("item1","variable1","item2","variable2"),remove=FALSE,sep="[.]") %>%
unite(group,variable1,variable2,sep=".")
library(ggrepel)
ggplot(mat,aes(x=common_EngGerm,y=common_GermAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_ItaAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_EngIta,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

Different correlation
plot and Pearson’s correlation
cowplot::plot_grid(p1,p2,p3,p4,p5,p6,nrow=3)

knitr::kable(cor_data)
| common_EngGerm-common_ItaAus |
common_EngGerm-common_ItaAus |
0.4538018 |
| common_EngGerm-common_GermAus |
common_EngGerm-common_GermAus |
0.4757044 |
| common_EngIta-common_GermAus |
common_EngIta-common_GermAus |
0.5091050 |
| common_EngIta-common_ItaAus |
common_EngIta-common_ItaAus |
0.5384113 |
| common_EngIta-common_EngGerm |
common_EngIta-common_EngGerm |
0.5957948 |
| common_GermAus-common_ItaAus |
common_GermAus-common_ItaAus |
0.5999612 |
All context
together
cov <- cor(filtered_conv[,likert_variables1],method = "pearson",use="pairwise.complete.obs")
row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity","educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="orange", id1="#f6e8c3",instru1="#35978f",necessity="#b35806",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
diag(cov) <- NA
pheatmap(cov, main = "All Contexts",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
, annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

Compare
correlations
library(GGally)
combine_cor <- merge(data_cor_eng_in_ita,data_cor_eng_in_germ,all = TRUE)
combine_cor1 <- merge(combine_cor,data_cor_germ_in_au,all = TRUE)
combine_cor2 <- merge(combine_cor1,data_cor_ita_in_au,all = TRUE)
combine_cor2 <- combine_cor2 %>% separate(var1,into=c("item1","variable1"),sep="[.]",remove=FALSE) %>%
separate(var2,into=c("item2","variable2"),sep="[.]",remove=FALSE) %>%
unite(group,variable1,variable2,sep=".")
Warning: Expected 2 pieces. Missing pieces filled with `NA` in 60 rows [133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, ...].Warning: Expected 2 pieces. Missing pieces filled with `NA` in 5 rows [101, 141, 393, 467, 498].
pairs(combine_cor2[,c("cor_eng_in_ita","cor_eng_in_germ","cor_germ_in_au","cor_ita_in_au")])

ggpairs(combine_cor2[,c("cor_eng_in_ita","cor_eng_in_germ","cor_germ_in_au","cor_ita_in_au")])

Correlation between
correlation in the different contexts
We will perform an exploratory FA combining all the contexts
together. This means that we are assuming that the correlation between
items across context has the same direction (does not happen that
cor(item1,item2)_context1 > 0 and cor(item1,item2)_context2 <
0).
common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))
[1] 0
sum(rownames(cov_EngIta) != rownames(cov_GermAus))
Warning: longer object length is not a multiple of shorter object length
[1] 4
sum(rownames(cov_EngIta) != rownames(cov_ItaAus))
Warning: longer object length is not a multiple of shorter object length
[1] 4
sum(rownames(cov_GermAus) != rownames(cov_ItaAus))
[1] 0
common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]
sum(rownames(common_EngIta) != colnames(common_EngIta))
[1] 0
sum(rownames(common_EngGerm) != colnames(common_EngGerm))
[1] 0
sum(rownames(common_GermAus) != colnames(common_GermAus))
[1] 0
sum(rownames(common_ItaAus) != colnames(common_ItaAus))
[1] 0
par(mfrow=c(2,3))
plot(common_EngIta,common_EngGerm)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngGerm,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngGerm,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_GermAus,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

# Largest differences
low_EngGerm <- lower.tri(common_EngGerm)
common_EngGerm[!low_EngGerm] <- NA
common_GermAus[!(lower.tri(common_GermAus))] <- NA
common_ItaAus[!(lower.tri(common_ItaAus))] <- NA
common_EngIta[!(lower.tri(common_EngIta))] <- NA
mat <- data.frame(common_EngGerm=c(common_EngGerm),
common_EngIta=c(common_EngIta),
common_GermAus = c(common_GermAus),
common_ItaAus = c(common_ItaAus),
compare = paste(rownames(common_EngGerm),colnames(common_EngGerm),sep=".")) %>%
filter(!is.na(common_EngGerm)) %>%
separate(compare,into=c("item1","variable1","item2","variable2"),remove=FALSE,sep="[.]") %>%
unite(group,variable1,variable2,sep=".")
library(ggrepel)
ggplot(mat,aes(x=common_EngGerm,y=common_GermAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_ItaAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_EngIta,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

Evaluate internal
consistency of known constructs with alpha
sets <- list(id.var=likert_variables1[grep("\\.id1$",likert_variables1)],
ought.var=likert_variables1[grep("\\.ought1$",likert_variables1)],
intr.var=likert_variables1[grep("\\.intr1$",likert_variables1)],
instru.var=likert_variables1[grep("\\.instru1$",likert_variables1)],
integr1.var=likert_variables1[grep("\\.integr1$",likert_variables1)],
prof.var=likert_variables1[grep("\\.prof1$",likert_variables1)],
post.var=likert_variables1[grep("\\.post1$",likert_variables1)],
comm.var=likert_variables1[grep("\\.comm1$",likert_variables1)])
get_alpha <- function(dataMot,
var=sets$id.var){
var_alpha <- psych::alpha(dataMot[,var])
dataf <- data.frame(alpha=var_alpha$total,
drop = var_alpha$alpha.drop)
rownames(dataf) <- rownames(var_alpha$alpha.drop)
return(dataf)
}
# "Italian in Australia"
ita_in_au_df <- filtered_conv[filtered_conv$Context == "Italian in Australia",]
list_of_ita_in_au <- lapply(sets,function(x) {
get_alpha(dataMot=ita_in_au_df,var=x)})
ita_in_au <- do.call(rbind,list_of_ita_in_au)
ita_in_au$var <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[1])
ita_in_au$var.full <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[3])
ita_in_au$Context <- "Italian in Australia"
rownames(ita_in_au) <- NULL
# "German in Australia"
germ_in_au <- do.call(rbind,lapply(sets,function(x) {
get_alpha(dataMot =filtered_conv[filtered_conv$Context == "German in Australia",],
var=x)}))
Warning: Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Some items ( knowledge.instru1 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
germ_in_au$var <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[1])
germ_in_au$var.full <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[3])
germ_in_au$Context <- "German in Australia"
rownames(germ_in_au) <- NULL
# "English in Germany"
eng_in_germ <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Germany",],
var=x)}))
Warning: Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Some items ( people.ought1 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
# the ones that makes issues
get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Germany",],
var=sets$ought.var)
Warning: Some items were negatively correlated with the total scale and probably
should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
Some items ( people.ought1 ) were negatively correlated with the total scale and
probably should be reversed.
To do this, run the function again with the 'check.keys=TRUE' option
eng_in_germ$var <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[1])
eng_in_germ$var.full <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[3])
eng_in_germ$Context <- "English in Germany"
rownames(eng_in_germ) <- NULL
# "English in Italy"
eng_in_ita <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Italy",],
var=x)}))
eng_in_ita$var <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[1])
eng_in_ita$var.full <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[3])
eng_in_ita$Context <- "English in Italy"
rownames(eng_in_ita) <- NULL
# combine
full_alpha <- rbind(eng_in_ita,eng_in_germ,germ_in_au,ita_in_au)
full_alpha %>% group_by(Context,var) %>%
summarise(st.alpha = unique(alpha.std.alpha),
G6=unique(alpha.G6.smc.)) %>%
ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw()
`summarise()` has grouped output by 'Context'. You can override using the `.groups` argument.

p2=ggplot(full_alpha,aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")
Error in ggplot(full_alpha, aes(x = var.full, y = drop.std.alpha, colour = Context)) :
object 'full_alpha' not found
```r
ggplot(full_alpha[full_alpha$var == \instru\,],aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + labs(y = \Drop Item - Chronbach's Alpha\, x = \Items\)
```
---
title: "Analysis of merged questionnaires"
author: "Anna Quaglieri & Riccardo Amorati"
date: "03/09/2017"
output:
  github_document:
    toc: yes
    toc_depth: 4
  html_document:
    toc: yes
    toc_depth: '4'
  html_notebook:
    code_folding: hide
    fig_caption: yes
    number_sections: yes
    toc: yes
    toc_float: yes
---

# Plan that I wrote with Richi's comments

Link at https://docs.google.com/document/d/1bdNeOMAYY90k8FAbPRWGBgS1YBEdP09kP0vcW0tNgPc/edit?usp=sharing


```{r,message=FALSE, echo=FALSE}
library(readr)
library(tidyverse)
library(reshape2)
library(corrplot)
library(psych)
library(pheatmap)
library(RColorBrewer)
library(cowplot)
library(gridExtra)
library(cowplot)
library(pheatmap)
library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(knitr)
library(readxl)

data(efc)
theme_set(theme_sjplot())

# Chunk options
knitr::opts_chunk$set(echo = TRUE, prompt = TRUE,cache = TRUE,fig.width = 12,fig.height = 12)

```

# Read in data

```{r message=FALSE,message=FALSE}
european <- read_csv("01-cleaning_data_data/european_recoded.csv")
australian <- read_csv("01-cleaning_data_data/australian_recoded.csv")

dim(european)
dim(australian)

european$EU <- 1
australian$AU <- 1

all <- merge(european,australian,all = TRUE)

table(all$EU,all$AU,useNA = "always")
```

## Variables to describe dataset (can be different between contexts)

```{r}
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]
```

- First language

```{r}
#table(all$L1,all$Context) # too many levels - needs to be cleaned (ex tot number of languages?)
table(all$L1,useNA = "always")
ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="")+theme_bw()
#table(all$speak.other.L2,all$Context)
```

```{r}
L2 <- data.frame(Freq=table(all$speak.other.L2)[order(table(all$speak.other.L2),decreasing = TRUE)],
                 L2=names(table(all$speak.other.L2))[order(table(all$speak.other.L2),decreasing = TRUE)]) # too many levels - needs to be cleaned (ex tot number of languages?)
head(L2)
```

- origins

```{r}
table(all$origins,useNA = "always")
```


```{r}
table(all$year.studyL2)
```

```{r}
table(all$degree)
```

- study.year in the European context is uni1.year in the Australian context

```{r}
all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
#table(all$study.year)
all$study.year <- ifelse(all$study.year == "Already graduated after 5 semesters in March 2016, was interested in survery/study, sorry.","6th semester",all$study.year)
table(all$study.year)
```

- proficiency

```{r}
table(all$prof,useNA = "always")
```

# Filter participants : keep only the ones that meet the inclusion criteria

```{r Filtered,fig.width=20,fig.height=15}
all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
# Filter only subject that we want to include in the study
# names(table(all$study.year))[1] = 1st semester"

filtered <- subset(all, (study.year == "1st year") | (study.year == names(table(all$study.year))[1]))
#& year.studyL2 != "0 years"

```


## Imputing degree based on Quality Comments provided in the questions

- **5313976716** : SCI (wish to study science in the future)
- **5359866545** : HUM.SCI (based on degree.other7)
- **5375370122** : SCI (she wants to do science)
- **5375376761** : HUM (she wants to do translation/reserach/teaching)

```{r}
filtered$degree[filtered$Resp.ID %in% "5313976716"] <- "SCI"
filtered$degree[filtered$Resp.ID %in% "5359866545"] <- "HUM.SCI"
filtered$degree[filtered$Resp.ID %in% "5375370122"] <- "SCI"
filtered$degree[filtered$Resp.ID %in% "5375376761"] <- "HUM"

table(filtered$degree)
kable(table(filtered$Context))
kable(table(filtered$year.studyL2,filtered$Context))
kable(table(filtered$year.studyL2,filtered$prof))
kable(table(filtered$year.studyL2,filtered$Context))
```

People that have studied 0 years L2 are just a small subset of the German in Australia and Italian in Australia context which means that by correcting for context we are not removing the effect of the 0 years. A way to remove the effect of the 0 years participants and not including too many variables could be to estimate the effect of 0 years vs all. 

# Define demographic variables

## Need to check datasets with Rcihi (why do we have QC in L1? Am I using not the latest dataset?)

```{r message=FALSE}
all <- filtered
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]

ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="") + theme_bw()

table(all$L1,all$Context)

table(all$degree,all$L1)
```

- Check for L1 but we decided not to filter for it

```{r}
#Filter by L1

nc <- names(table(all$Context))
table(all$Context)
l1_filter <- all[(all$Context == nc[1] & (all$L1 == "German" | all$L1 == "German and English")) | 
                    (all$Context == nc[2] & (all$L1 == "Italian")) | 
                    (all$Context == nc[3] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")) |
                    (all$Context == nc[4] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")),]


#all <- l1_filter

# do not filter for L1
all <- all

# subset demographics
demo <- subset(all,select=c("Resp.ID",demographics_var,l2School_variables))

# Numeri finali
table(l1_filter$Context)
table(all$Context)

```

- Filter missing value:
  - Filter participants who didn't put the degree
  - we don't care about speak.other.L2 and study.other.L2

```{r}
missing_bySample <- rowSums(is.na(demo))
names(missing_bySample) <- demo$Resp.ID
missing_byVar <- colSums(is.na(demo))
names(missing_byVar) <- colnames(demo)

barplot(missing_bySample)
d <- data.frame(miss=missing_byVar)
d$varID <- rownames(d)
ggplot(data=d,aes(x=varID,y=miss)) + geom_bar(stat="identity") + theme_bw() +theme(axis.text.x = element_text(angle = 45, hjust = 1)) 


demo_missing <- demo %>% group_by(Context) %>% summarise(roleL2.degree_na = sum(is.na(roleL2.degree)),
                                                         L2.VCE_na = sum(is.na(L2.VCE)),
                                                         other5.other.ways_na=sum(is.na(other5.other.ways )),
                                                         uni1.year_na = sum(is.na(uni1.year)),
                                                         primary1.L2school_na=sum(is.na(primary1.L2school)),
                                                         CLS3.L2school_na = sum(is.na(CLS3.L2school)),
                                                         VSL4.L2school_na=sum(is.na(VSL4.L2school)),
                                                         degree = sum(is.na(degree)),
                                                         schooL2country5.L2school_na=sum(is.na(schooL2country5.L2school)))

# We do not filter for speak.other.L2 or study.other.L2

#demo[is.na(demo$speak.other.L2),]
# teniamo
#demo[is.na(demo$study.other.L2),]
missing_bySample[names(missing_bySample) == "5166861581"]
#demo[is.na(demo$year.studyL2),]
missing_bySample[names(missing_bySample) == "5378798787"]

# remove NA from degree
#table(demo$degree,useNA = "always")
# Remove people
all <- all[!is.na(all$degree),]
```

## Stats about filtered dataset

```{r}
kable(table(all$Context))
kable(table(all$study.year))
kable(table(all$year.studyL2))
```

## Recoded demographic variables

```{r}
recoded_dem_richi <- read_excel("02-descriptive_data/21 03 merged_filtered_imputedMedian_likertNumber.xlsx")
```


## Write filtered and merged dataset

```{r}
write.csv(all,file.path("02-descriptive_data/context-merged_filtered.csv"))
```

## Descriptive plots and tables

```{r speak.other.L2_binary,message=FALSE}
all$speak.other.L2_binary <- ifelse(!is.na(all$speak.other.L2) & 
                                      !(all$speak.other.L2 %in% c("Yes","No")),"Yes",as.character(all$speak.other.L2))


kable(table(all$speak.other.L2_binary,all$Context,useNA = "always"))

```

- Age

```{r age_by_context,message=FALSE}
tabAge <- t(table(all$Age,all$Context))
ggdf <- data.frame(Age = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
  N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
  Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])

ggplot(ggdf,aes(x=Age,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by age")+
  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

```

```{r gender_by_context,message=FALSE}
# add numbers on the bar

tabAge <- t(table(all$Gender,all$Context))
ggdf <- data.frame(Gender = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
  N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
  Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])


ggplot(ggdf,aes(x=Gender,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by gender")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

```

```{r origns_by_context,message=FALSE}
# add numbers on the bar
tabAge <- t(table(all$origins,all$Context))
ggplot(all,aes(x=origins,fill=Context)) + geom_bar(position="dodge",colour="white") + ggtitle("Origins by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=60, width=0.3, height=0.4) + ggtitle("Participants by origins")
tabAge
```

- proficiency

```{r proficiency_by_context,message=FALSE}
tabAge <- t(table(all$prof,all$Context))
ggplot(all,aes(x=Context,fill=prof)) + geom_bar(position="dodge",colour="white") + ggtitle("Proficiency by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)
tabAge
```

- L2.VCE

```{r L2VCE_by_context,message=FALSE}
tabAge <- t(table(all[all$Context != "English in Germany" & all$Context != "English in Italy","L2.VCE"],all[all$Context != "English in Germany" & all$Context != "English in Italy",'Context'],useNA = "always"))
tabAge <- tabAge[-3,]

ggplot(all[all$Context != "English in Germany" & all$Context != "English in Italy",],aes(x=Context,fill=L2.VCE)) + geom_bar(position="dodge",colour="white") + ggtitle("L2.VCE by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)
```

- da mettere a posto con Richi

```{r year.studyL2_European_context,message=FALSE}
# year study L2
table(all$year.studyL2,all$other.year.studyL2.richi)

all$year.studyL2 <- ifelse(all$year.studyL2 == "Other",all$other.year.studyL2.richi,all$year.studyL2 )

# European context
ggplot(all[all$Context == "English in Germany" | all$Context == "English in Italy",],aes(x=degree,fill=year.studyL2)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree by study year L2, by Context") +  facet_grid(~Context,scales="free") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(y = "N participants", x = "degree")
```

- Degree of enrolment

```{r degree_by_context,message=FALSE}
# Australian context
tabAge <- t(table(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'degree'],all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'Context']))
ggplot(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in Australian Contexts") + draw_grob(tableGrob(tabAge), x=1., y=40, width=0.3, height=0.4)
tabAge
```

```{r year.studyL2_Australian_context,message=FALSE}
# Australian context
tabAge <- t(table(all[all$Context == "English in Italy" | all$Context == "English in Germany",'degree'],all[all$Context == "English in Italy" | all$Context == "English in Germany",'Context']))

ggplot(all[all$Context == "English in Italy" | all$Context == "English in Germany",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in European Contexts")

tabAge
```


# Australian context spcific variables 

```{r}
kable(table(all$reconnect.comm,all$Context))
kable(table(all$speakersmelb.comm,all$Context))
kable(table(all$comecloser.comm,all$Context))
```


\clearpage

# Likert scales

```{r include=FALSE,message=FALSE}
likert_grep <- "\\.id$|\\.ought$|\\.intr$|\\.instru$|\\.integr$|\\.prof$|\\.post$|\\.comm$|^necessity$|^educated$"

# all
likert_variables_all <- colnames(all)[grep(likert_grep,colnames(all))]
likert_variables_all
likert_variables_all <- likert_variables_all[!(likert_variables_all %in% "other.prof")]

```

- **Convert Likert scales to numbers**

```{r fig.width=15,fig.height=15}

convertToNumber <- function(column){
  column <- factor(column,levels = c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
  column_number <- as.numeric(column)
  return(column_number)
}

table(all$Context)
table(all$study.year)

convert_likert <- data.frame(apply(subset(all,select=likert_variables_all),2,convertToNumber))
colnames(convert_likert) <- paste0(colnames(convert_likert),"1")

likert_variables1 <- paste0(likert_variables_all,"1")

# join the converted variables to the filtered dataset
filtered_conv <- cbind(all,convert_likert)

table(filtered_conv[,likert_variables_all[4]],filtered_conv[,likert_variables1[4]],useNA = "always")

write.csv(filtered_conv,"02-descriptive_data/merged_filtered_likertNumber.csv",row.names = FALSE)
```

## Impute missing values - Using median values

The missing values appears to be at random and there are max two missing values in one variable (see plots below). In order not to loose 12 participants while doing the factor analysis across contexts it is preferable to impute the 12 missing values. 

```{r}
all <- filtered_conv

# Items to use for factor analysis : items shared between contexts
# items to be used for the FA

usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
```


```{r}
rownames(all) <- all$Resp.ID
usable_data_context <- all[,c(usable_items,"Context")]
dat_noNA <- usable_data_context[rowSums(is.na(usable_data_context)) == 0,]
all_noNA <- all[rowSums(is.na(usable_data_context)) == 0,]
table(rowSums(is.na(usable_data_context)))
# Participants with NA to remove
table(rowSums(is.na(usable_data_context)),usable_data_context$Context,useNA = "always")

# Variable missing values
table(colSums(is.na(usable_data_context)))
table(rowSums(is.na(usable_data_context)),usable_data_context$Context,useNA = "always")

# check what to use to impute
# have a look at the distribution of missing values
library(mice)
library(VIM)

mice_plot <- aggr(usable_data_context[,usable_items], col=c('navyblue','yellow'),
                    numbers=TRUE, sortVars=TRUE,
                    labels=names(usable_data_context[,usable_items]), cex.axis=.4,
                    gap=1, ylab=c("Missing data","Pattern"),cex.numbers=0.5)

# Imputing using median
library(Hmisc)
imputedMedian <- usable_data_context

imputedMedian$globalaccess.post1 <- with(imputedMedian[,usable_items], impute(globalaccess.post1, median))
imputedMedian$citizen.post1 <- with(imputedMedian[,usable_items], impute(citizen.post1, median))
imputedMedian$money.instru1 <- with(imputedMedian[,usable_items], impute(money.instru1, median))
imputedMedian$knowledge.instru1 <- with(imputedMedian[,usable_items], impute(knowledge.instru1, median))
imputedMedian$life.intr1 <- with(imputedMedian[,usable_items], impute(life.intr1, median))
imputedMedian$time.integr1 <- with(imputedMedian[,usable_items], impute(time.integr1, median))
imputedMedian$expect.ought1 <- with(imputedMedian[,usable_items], impute(expect.ought1, median))
imputedMedian$job.instru1 <- with(imputedMedian[,usable_items], impute(job.instru1, median))
imputedMedian$career.instru1 <- with(imputedMedian[,usable_items], impute(career.instru1, median))
imputedMedian$meeting.integr1 <- with(imputedMedian[,usable_items], impute(meeting.integr1, median))
imputedMedian$interact.post1 <- with(imputedMedian[,usable_items], impute(interact.post1, median))

# check before after
table(imputedMedian$time.integr1)
table(usable_data_context$time.integr1)

table(imputedMedian$life.intr1)
table(usable_data_context$life.intr1)

table(imputedMedian$knowledge.instru1)
table(usable_data_context$knowledge.instru1)

table(imputedMedian$money.instru1)
table(usable_data_context$money.instru1)

table(imputedMedian$citizen.post1)
table(usable_data_context$citizen.post1)

table(imputedMedian$globalaccess.post1)
table(usable_data_context$globalaccess.post1)


table(imputedMedian$expect.ought1)
table(usable_data_context$expect.ought1)

table(imputedMedian$job.instru1)
table(usable_data_context$job.instru1)

table(imputedMedian$career.instru1)
table(usable_data_context$career.instru1)

table(imputedMedian$meeting.integr1)
table(usable_data_context$meeting.integr1)

table(imputedMedian$interact.post1)
table(usable_data_context$interact.post1)


```

- Substitute imputed data for the common variables to be used in the Factor Analysis

```{r}
all <- all[,!(colnames(all) %in% usable_items)]
imputedMedian$Context <- NULL
sum(!(colnames(imputedMedian) %in% usable_items))
all <- cbind(all,imputedMedian[match(rownames(imputedMedian),all$Resp.ID),])
```

**Add some updates that Richi did in Date 7th June 2018**

```{r eval=FALSE}
Other_ways_and_degree_role_with_respondent_IDs <- read_excel("Other-ways-and-degree-role-with-respondent-IDs.xlsx")
sum(Other_ways_and_degree_role_with_respondent_IDs$Resp.ID != Other_ways_and_degree_role_with_respondent_IDs$Resp.ID__1)
# to replace 
# match for the NA degree.role

match_updates <- match(all$Resp.ID,Other_ways_and_degree_role_with_respondent_IDs$Resp.ID)
all$private.lessons1.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$private.lessons1.other.ways
all$study.holiday2.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$study.holiday2.other.ways
all$year.sem.abroad3.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$year.sem.abroad3.other.ways
all$online.course4.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$online.course4.other.ways
all$other5.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$other5.other.ways
all$degree.role[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$degree.role
```

## Save imputed data

```{r}
write.csv(all,"02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv",row.names = FALSE)
```

# Barplot of likert variables

```{r fig.width=20,fig.height=20}
all_melt <- melt(all,id.vars = c("Resp.ID","Gender","Age","prof","Context","study.year"),
                        measure.vars = likert_variables1)

all_melt$value <- factor(all_melt$value,levels=c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
# dim(all_melt)
# 323*length(likert_variables1)

all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)
ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack",colour="black") + 
  facet_grid(Context~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + ggtitle("Filtered dataset") + scale_fill_manual(values=c("#ca0020","#f4a582","#ffffbf","#abd9e9","#2c7bb6","grey"))

filt_sum <- all_melt %>% group_by(Context,variable,type,value) %>% dplyr::summarise(Ngroup=length(value))
ggplot(filt_sum,aes(x=value,y=Ngroup,colour=Context,group=interaction(variable, Context))) + geom_line() + geom_point() + facet_wrap(~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1))

```

## Barplot of Educated and Necessity in the Australian and European Contexts

- **Educated**

```{r}
# add numbers on the bar
educated <- all[all$Context %in% c("German in Australia","Italian in Australia"),]
table(educated$educated1,educated$Context,useNA="always")
educated$educated1 <- factor(educated$educated1,levels = c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree")) 

tabEdu <- t(table(educated$educated1,educated$Context))
ggdf <- data.frame(Educated = rep(colnames(tabEdu),each=2),
  N.Participants = as.numeric(tabEdu),
  Context = rep(rownames(tabEdu),times=5))


ggplot(ggdf,aes(x=Educated,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Educated by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 
ggplot(ggdf,aes(x=Context,y=N.Participants,fill=Educated)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Educated by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 
```

- **Necessity**

```{r}
# add numbers on the bar
necessity <- all[all$Context %in% c("English in Germany","English in Italy"),]
table(necessity$necessity1,necessity$Context,useNA="always")
necessity$necessity1 <- factor(necessity$necessity1,levels = c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree")) 

tabNec <- t(table(necessity$necessity1,necessity$Context,useNA = "always"))[-3,]
ggdf <- data.frame(Necessity = rep(colnames(tabNec),each=2),
  N.Participants = as.numeric(tabNec),
  Context = rep(rownames(tabNec),times=6))


ggplot(ggdf,aes(x=Necessity,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,40,10),limits=c(0,40)) + theme_bw() + ggtitle("Necessity by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 
ggplot(ggdf,aes(x=Context,y=N.Participants,fill=Necessity)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Necessity by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 
```

# Correlation plot of items by context

## Italian in Australia

```{r cor_italian_in_australia,fig.width=15,fig.height=15}
cov <- cor(filtered_conv[filtered_conv$Context == "Italian in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
data_cor_ita_in_au <- data.frame(cor_ita_in_au=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))

row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

#pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE],  annotation_colors = ann_colors_wide,breaks=seq(-1,1,0.2),col=c("#67001f","#b2182b","#d6604d","#f4a582","#fddbc7","#f7f7f7","#d1e5f0","#92c5de","#4393c3","#2166ac","#053061"),show_colnames = FALSE,width = 7,height = 7)
###################

diag(cov) <- NA
pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

cov_ItaAus <- cov
```

## German in Australia

```{r cor_german_in_australia,fig.width=15,fig.height=15}
cov <- cor(filtered_conv[filtered_conv$Context == "German in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
data_cor_germ_in_au <- data.frame(cor_germ_in_au=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))



row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "German in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

# 
cov_GermAus <- cov
```

## English in Germany

```{r cor_english_in_germany,fig.width=15,fig.height=15}
cov <- cor(filtered_conv[filtered_conv$Context == "English in Germany",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1",    "speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
data_cor_eng_in_germ <- data.frame(cor_eng_in_germ=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))


row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(id1="#f6e8c3",necessity="#b35806",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "English in Germany",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

cov_EngGerm <- cov
```

## English in Italy

```{r cor_english_in_italy,fig.width=15,fig.height=15}
cov <- cor(filtered_conv[filtered_conv$Context == "English in Italy",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1","speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
data_cor_eng_in_ita <- data.frame(cor_eng_in_ita=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))


row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- "necessity"
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",necessity="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "English in Italy",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

# 
cov_EngIta <- cov
```

## Correlation between correlation in the different contexts

We will perform an exploratory FA combining all the contexts together. This means that we are assuming that the correlation between items across context has the same direction (does not happen that cor(item1,item2)_context1 > 0 and cor(item1,item2)_context2 < 0). 

```{r fig.width=10,fig.height=10}
common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))
sum(rownames(cov_EngIta) != rownames(cov_GermAus))
sum(rownames(cov_EngIta) != rownames(cov_ItaAus))
sum(rownames(cov_GermAus) != rownames(cov_ItaAus))

common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]

sum(rownames(common_EngIta) != colnames(common_EngIta))
sum(rownames(common_EngGerm) != colnames(common_EngGerm))
sum(rownames(common_GermAus) != colnames(common_GermAus))
sum(rownames(common_ItaAus) != colnames(common_ItaAus))


par(mfrow=c(2,3))
plot(common_EngIta,common_EngGerm)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngGerm,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngGerm,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_GermAus,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

# Largest differences
low_EngGerm <- lower.tri(common_EngGerm)
common_EngGerm[!low_EngGerm] <- NA
common_GermAus[!(lower.tri(common_GermAus))] <- NA
common_ItaAus[!(lower.tri(common_ItaAus))] <- NA
common_EngIta[!(lower.tri(common_EngIta))] <- NA


mat <- data.frame(common_EngGerm=c(common_EngGerm),
                  common_EngIta=c(common_EngIta),
                  common_GermAus = c(common_GermAus),
                  common_ItaAus = c(common_ItaAus),
                  compare = paste(rownames(common_EngGerm),colnames(common_EngGerm),sep=".")) %>%
  filter(!is.na(common_EngGerm)) %>%
  separate(compare,into=c("item1","variable1","item2","variable2"),remove=FALSE,sep="[.]") %>%
  unite(group,variable1,variable2,sep=".")

library(ggrepel)
ggplot(mat,aes(x=common_EngGerm,y=common_GermAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")


ggplot(mat,aes(x=common_EngGerm,y=common_ItaAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_EngIta,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

```


## Different correlation plot and Pearson's correlation

```{r fig.width=12,fig.height=8, include=FALSE}
common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))
sum(rownames(cov_EngIta) != rownames(cov_GermAus))
sum(rownames(cov_EngIta) != rownames(cov_ItaAus))
sum(rownames(cov_GermAus) != rownames(cov_ItaAus))

common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]

sum(rownames(common_EngIta) != colnames(common_EngIta))
sum(rownames(common_EngGerm) != colnames(common_EngGerm))
sum(rownames(common_GermAus) != colnames(common_GermAus))
sum(rownames(common_ItaAus) != colnames(common_ItaAus))

contexts_df <- data.frame(x = c("common_EngIta","common_EngIta","common_EngIta",
"common_EngGerm","common_EngGerm",
"common_GermAus"),
                          y = c("common_EngGerm","common_ItaAus","common_GermAus",
                                "common_GermAus","common_ItaAus",
                               "common_ItaAus")) %>%
  tidyr::unite(name,x,y,sep="-",remove=FALSE)

scor <- vector("list",length=nrow(contexts_df))
names(scor) <- contexts_df$name
names_plot <- paste0("p",1:nrow(contexts_df))
names_plot1 <- paste0("p_var1_",1:nrow(contexts_df))
names_plot2 <- paste0("p_var2_",1:nrow(contexts_df))

for(i in 1:nrow(contexts_df)) {
  
  comp <- contexts_df[i,]
  cont1 <- get(as.character(comp$x))
  cont2 <- get(as.character(comp$y))
  
  names_comp <- data.frame(item1 = rep(colnames(cont1),each=ncol(cont1)),
                         item2 = rep(colnames(cont1),
                        times=ncol(cont1))) %>%
  tidyr::unite(name_comp, item1,item2,sep="-",remove=FALSE) %>%
  tidyr::unite(name_comp1, item2,item1,sep="-",remove=FALSE) %>%
  dplyr::mutate(cont1_vec = c(cont1),
                cont2_vec = c(cont2)) %>%
  dplyr::filter(name_comp != name_comp1) %>%
    tidyr::separate(item1 , into=c("type1","variable1"),sep="[.]",remove=FALSE) %>%
    tidyr::separate(item2 , into=c("type2","variable2"),sep="[.]",remove=FALSE)

scor[[i]] = cor(names_comp$cont1_vec,names_comp$cont2_vec)
assign(names_plot[i],ggplot(names_comp,aes(x=cont1_vec,y=cont2_vec)) + geom_hex()+
  geom_vline(xintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") +
  geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")+
  labs(x=paste0(gsub("common_","",as.character(comp$x))),y=gsub("common_","",as.character(comp$y))))
  
assign(names_plot1[i],ggplot(names_comp,aes(x=cont1_vec,y=cont2_vec,colour=variable1)) + geom_point()+
  geom_vline(xintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") +
  geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")+
  labs(x=paste0(gsub("common_","",as.character(comp$x))),y=gsub("common_","",as.character(comp$y))))

  assign(names_plot2[i],ggplot(names_comp,aes(x=cont1_vec,y=cont2_vec,colour=variable2)) + geom_point()+
  geom_vline(xintercept = c(-0.3,0.3),linetype="dotted",colour="dark red") +
  geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")+
  labs(x=paste0(gsub("common_","",as.character(comp$x))),y=gsub("common_","",as.character(comp$y))))

print(i)
  
}

cor_data <- data.frame(comparison = names(scor),r_squared = do.call(c,scor)) %>% 
  arrange(r_squared)
```

```{r fig.width=12,fig.height=8}
cowplot::plot_grid(p1,p2,p3,p4,p5,p6,nrow=3)
```

```{r}
knitr::kable(cor_data)
```


## All context together

```{r cor_all_contexts}
cov <- cor(filtered_conv[,likert_variables1],method = "pearson",use="pairwise.complete.obs")

row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity","educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="orange", id1="#f6e8c3",instru1="#35978f",necessity="#b35806",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "All Contexts",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

```

## Compare correlations

```{r}
library(GGally)
combine_cor <- merge(data_cor_eng_in_ita,data_cor_eng_in_germ,all = TRUE)
combine_cor1 <- merge(combine_cor,data_cor_germ_in_au,all = TRUE)
combine_cor2 <- merge(combine_cor1,data_cor_ita_in_au,all = TRUE)
combine_cor2 <- combine_cor2 %>% separate(var1,into=c("item1","variable1"),sep="[.]",remove=FALSE) %>%
  separate(var2,into=c("item2","variable2"),sep="[.]",remove=FALSE) %>%
  unite(group,variable1,variable2,sep=".")

pairs(combine_cor2[,c("cor_eng_in_ita","cor_eng_in_germ","cor_germ_in_au","cor_ita_in_au")])
ggpairs(combine_cor2[,c("cor_eng_in_ita","cor_eng_in_germ","cor_germ_in_au","cor_ita_in_au")])

```


## Correlation between correlation in the different contexts

We will perform an exploratory FA combining all the contexts together. This means that we are assuming that the correlation between items across context has the same direction (does not happen that cor(item1,item2)_context1 > 0 and cor(item1,item2)_context2 < 0). 

```{r fig.width=10,fig.height=10}
common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))
sum(rownames(cov_EngIta) != rownames(cov_GermAus))
sum(rownames(cov_EngIta) != rownames(cov_ItaAus))
sum(rownames(cov_GermAus) != rownames(cov_ItaAus))

common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]

sum(rownames(common_EngIta) != colnames(common_EngIta))
sum(rownames(common_EngGerm) != colnames(common_EngGerm))
sum(rownames(common_GermAus) != colnames(common_GermAus))
sum(rownames(common_ItaAus) != colnames(common_ItaAus))


par(mfrow=c(2,3))
plot(common_EngIta,common_EngGerm)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngGerm,common_GermAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngGerm,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_GermAus,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

# Largest differences
low_EngGerm <- lower.tri(common_EngGerm)
common_EngGerm[!low_EngGerm] <- NA
common_GermAus[!(lower.tri(common_GermAus))] <- NA
common_ItaAus[!(lower.tri(common_ItaAus))] <- NA
common_EngIta[!(lower.tri(common_EngIta))] <- NA


mat <- data.frame(common_EngGerm=c(common_EngGerm),
                  common_EngIta=c(common_EngIta),
                  common_GermAus = c(common_GermAus),
                  common_ItaAus = c(common_ItaAus),
                  compare = paste(rownames(common_EngGerm),colnames(common_EngGerm),sep=".")) %>%
  filter(!is.na(common_EngGerm)) %>%
  separate(compare,into=c("item1","variable1","item2","variable2"),remove=FALSE,sep="[.]") %>%
  unite(group,variable1,variable2,sep=".")

library(ggrepel)
ggplot(mat,aes(x=common_EngGerm,y=common_GermAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")


ggplot(mat,aes(x=common_EngGerm,y=common_ItaAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_EngIta,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

```



# Evaluate internal consistency of known constructs with alpha

```{r}
sets <- list(id.var=likert_variables1[grep("\\.id1$",likert_variables1)],
             ought.var=likert_variables1[grep("\\.ought1$",likert_variables1)],
             intr.var=likert_variables1[grep("\\.intr1$",likert_variables1)],
             instru.var=likert_variables1[grep("\\.instru1$",likert_variables1)],
             integr1.var=likert_variables1[grep("\\.integr1$",likert_variables1)],
             prof.var=likert_variables1[grep("\\.prof1$",likert_variables1)],
             post.var=likert_variables1[grep("\\.post1$",likert_variables1)],
             comm.var=likert_variables1[grep("\\.comm1$",likert_variables1)])
              

get_alpha <- function(dataMot,
                      var=sets$id.var){
  var_alpha <- psych::alpha(dataMot[,var])
  dataf <- data.frame(alpha=var_alpha$total,
                    drop = var_alpha$alpha.drop)
  rownames(dataf) <- rownames(var_alpha$alpha.drop)
  return(dataf)
}

# "Italian in Australia"
ita_in_au_df <- filtered_conv[filtered_conv$Context == "Italian in Australia",]
list_of_ita_in_au <- lapply(sets,function(x) {
  get_alpha(dataMot=ita_in_au_df,var=x)})
ita_in_au <- do.call(rbind,list_of_ita_in_au)
ita_in_au$var <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[1]) 
ita_in_au$var.full <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[3]) 
ita_in_au$Context <- "Italian in Australia"
rownames(ita_in_au) <- NULL

# "German in Australia"
germ_in_au <- do.call(rbind,lapply(sets,function(x) {
  get_alpha(dataMot =filtered_conv[filtered_conv$Context == "German in Australia",],
                      var=x)}))
germ_in_au$var <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[1]) 
germ_in_au$var.full <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[3]) 
germ_in_au$Context <- "German in Australia"
rownames(germ_in_au) <- NULL

# "English in Germany"
eng_in_germ <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
  get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Germany",],
                      var=x)}))

# the ones that makes issues
get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Germany",],
                      var=sets$ought.var)

eng_in_germ$var <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[1]) 
eng_in_germ$var.full <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[3]) 
eng_in_germ$Context <- "English in Germany"
rownames(eng_in_germ) <- NULL

# "English in Italy"
eng_in_ita <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
  get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Italy",],
                      var=x)}))
eng_in_ita$var <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[1]) 
eng_in_ita$var.full <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[3]) 
eng_in_ita$Context <- "English in Italy"
rownames(eng_in_ita) <- NULL


# combine
full_alpha <- rbind(eng_in_ita,eng_in_germ,germ_in_au,ita_in_au)

```

- Plot alpha by variable

```{r alpha_chronbach_by_context}

full_alpha %>% group_by(Context,var) %>% 
  summarise(st.alpha = unique(alpha.std.alpha),
            G6=unique(alpha.G6.smc.)) %>%
  ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw()

```


```{r fig.height=18,fig.width=14}
all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)
p1=ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack") + 
  facet_grid(Context~type,scales = "free") + ggtitle("Filtered dataset")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8))+theme_bw()

p2=ggplot(full_alpha,aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")

p4=ggplot(full_alpha,aes(x=var.full,y=drop.average_r,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")

p3=full_alpha %>% group_by(Context,var) %>% 
  summarise(st.alpha = unique(alpha.std.alpha),
            G6=unique(alpha.G6.smc.)) %>%
  ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + theme_bw()


cowplot::plot_grid(p2,p3,nrow=2)

```


```{r}
ggplot(full_alpha[full_alpha$var == "instru",],aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + labs(y = "Drop Item - Chronbach's Alpha", x = "Items") + ggtitle("Variable: Instrumental Orientation")

```

